Isotopic Tracing of Stormwater in the Urban Liesbeek River

Short communication Isotopic tracing of stormwater in the urban Liesbeek River

Ruan van Mazijk1 , Lucy K Smyth1,2 , Eleanor A Weideman1,3 and Adam G West1* 1Department of Biological Sciences, University of Cape Town, Rondebosch, South Africa 2Institute for Communities and Wildlife in Africa (iCWild), University of Cape Town, Rondebosch, South Africa 3FitzPatrick Institute of African Ornithology, University of Cape Town, Rondebosch, South Africa

ABSTRACT The ongoing drought in the Western Cape of South Africa (2014 to present) has called for an urgent need to improve our understanding of water resources in the area. Rivers within the Western Cape are known to surge rapidly after rainfall events. Such storm-flow in natural river catchments in the Jonkershoek mountains has previously been shown to be driven by displaced groundwater, with less than 5% of rainfall appearing in the storm-flow. However, the origin of storm-flow surges within urban rivers in the region remains unknown. In this study, we used stable isotopes in water to illustrate that at least 90% of water in the Liesbeek River during a storm event was rainwater. There was a strong correlation between storm-flow and rainfall rates (P < 0.001, Pearson’s r = 0.86), as well as between the δ18O and δ2H values of river-water and rainwater (δ18O: Pearson’s r = 0.741 (P = 0.001), δ2H: Pearson’s r = 0.775 (P < 0.001)). Storm-flow within this urban river therefore appears to be driven by overland-flow over the hardened urban catchment, rather than piston-flow as seen in natural catchments. Our results support studies suggesting the Liesbeek River could be a target for stormwater harvesting to augment water resources in the city of Cape Town.

Keywords: stable isotopes, urban water management, water resources, urban rivers

INTRODUCTION the ground, displacing groundwater which then flows into the Water resources in the Western Cape of South Africa are scarce river (Sophocleous, 2002). Piston-flow is an important process and are predicted to become increasingly so in the future for aquifer recharge, but may result in delayed storm-flow after (Otieno and Ochieng, 2004). At the time of this study the City extensive aquifer drawdown (McGuire et al., 2002). However, of Cape Town was poised to become the first major city to run while piston-flow may generally be the predominant pathway out of water (Du Toit, 2018). Currently, the cities within this for storm-flow in natural, fractured sandstone catchments predominately winter-rainfall region rely on surrounding dams (Midgley and Scott, 1994; Midgley et al., 2001), little is known for their water supply, which are generally replenished through about the dynamics of storm-flow from other rivers in this winter streamflow. During drought years, streamflow recharge region, particularly urban areas where substrate permeability is into dams is severely diminished (Mukheibir and Ziervogel, low. In such areas, overland-flow should predominate, resulting 2007). Extended below-average rainfall in the region since in rainwater directly recharging streams without sinking into 2014 led to severe water shortages in the City of Cape Town the ground to recharge aquifers (Burns et al., 2001). Unlike for and in 2017 threatened the potential collapse of the city’s water piston-flow, overland-flow should result in rapid storm-flow infrastructure (so-called ‘DayZero’). In order to secure future surges even in the case where aquifers have been drawn down water supply, cities in the Western Cape will have to adapt to substantially during prolonged drought. the likelihood of reduced rainfall in the future (New, 2002; Additionally, unless overland-flow-driven streams are Ziervogel et al., 2011), as well as augment their water supply captured in dams or water storage schemes, usually not the case from alternative sources such as aquifers and stormwater for low-elevation urban rivers, this water will eventually be lost amongst others (New, 2002; Fisher-Jeffes et al., 2017). As such, to the sea without the benefit of aquifer recharge (Frazer, 2005; understanding the storm-flow dynamics of rivers in this region Saraswat et al., 2016). In June 2017, during a prolonged 3-year and their relationship to rainfall is important, to properly drought, a large storm hit Cape Town resulting in severe winds manage the area’s water resources. and precipitation across the city. We used this opportunity Rivers in the Western Cape are known to rapidly increase to determine whether overland or piston-flow predominated in flow following rainfall. Midgley and Scott (1994) showed in the urban Liesbeek River and to assess the stormwater that streamflow in rivers in the Jonkershoek mountains surged discharge occurring during a drought. The source of the within a few hours of the first rains. Using stable isotopes, increase in storm-flow was determined using stable isotopes of 2 18 they demonstrated that despite flow increasing by 400%, the hydrogen (δ H (‰)) and oxygen (δ O (‰)) of river-water and storm-flow consisted of less than 5% of the rainfall event, rainfall (McGuire and McDonnel, 2007). We reasoned that a leading to the conclusion that these rivers were fuelled mainly predominance of overland-flow would result in rainfall and by piston-flow. Piston-flow occurs when rainwater seeps into storm-flow closely tracking each other throughout the storm event. However, if rainfall and storm-flow were isotopically dissimilar, or showed strong lags, then piston-flow was more * To whom all correspondence should be addressed. likely to predominate.  +27 21 650 3628; We hypothesized that despite the mountain headwaters, e-mail: [email protected] this section of river flowing through the urban environment Received 14 February 2018, accepted in revised form 25 September 2018. would be driven primarily by overland-flow, due to the extensive

http://dx.doi.org/10.4314/wsa.v44i4.16 Available on website http://www.wrc.org.za ISSN 1816-7950 (Online) = Water SA Vol. 44 No. 4 October 2018 Published under a Creative Commons Attribution Licence 674 hardening of its urban catchment. If so, this would have Packaging, Neenah, WI 54956, USA) and transported to implications for the effective future use of stormwater in this the laboratory for analysis. The rain gauge was emptied and river to augment the City of Cape Town’s scarce water resources. reset until the next collection. For practical purposes, the UCT and Skeleton Gorge rain METHODS gauges were only assessed after the storm had passed. As such, these only reflect total rainfall and isotopic composition Study area at those stations. No attempt was made to limit evaporative losses from the rain gauges during collection as these gauges The Liesbeek River originates in the natural Table Mountain were only exposed to the atmosphere during the storm, when sandstone catchment above Kirstenbosch but then flows evaporative losses would be minimal, and were sampled through the southern suburbs of Cape Town (Fig. 1), where immediately thereafter. All isotope analyses were conducted approximately 40% of its channel has been canalised (River within 24 hours of sample collection. Health Programme, 2005; Brown and Magoda, 2009). It has River-water was sampled from a weir in a canalised a total catchment area of 327 km2 and flows most strongly section of the Liesbeek River in Mowbray (33° 56′ 48.23″ S during the wet winter months. Flow is reduced during summer 18° 28′ 39.69″ E, 4 m amsl). Samples were taken prior to the months, with the shallow upper and middle reaches often storm event, at multiple intervals throughout the storm (in drying completely (Suri et al., 2017). conjunction with Mowbray rainfall) and for 8 h after the storm had passed. River-water samples were collected from Sampling methods below the water’s surface in a fast-flowing section mid-stream. Simultaneously, streamflow measurements were obtained by Prior to the storm, rain gauges were placed at the river measuring the time (Δt) it took a nearly submerged floating sampling point in Mowbray (33° 56′ 48.23″ S 18° 28′ object to travel a fixed distance Δx( ) down the middle of the 39.69″ E, 11 m amsl), at the University of Cape Town’s Upper channel. We used small oranges as floating objects as they Campus (UCT, 33° 57′ 24.30″ S 18° 27′ 40.00″ E, 120 m amsl) were visible at night and approximately the density of water and at the top of Skeleton Gorge on Table Mountain (the and thus mostly submerged and unaffected by wind. The Liesbeek’s headwaters, 33° 58′ 42.38″ S 18° 24′ 48.42″ E, depth of the water in the canalized channel (d) and breadth of 820 m amsl). Periodic measurement of accumulated rainfall the channel (b) was also measured. From these measurements, amount and isotopic composition were recorded at the streamflow rate ν( ) (m3·h-1) was calculated as: Mowbray gauge throughout the storm event. After recording rainfall amount, rainwater samples were decanted into 10 = × × (1) mL centrifuge vials, sealed with Parafilm (Bemis Flexible ∆𝑥𝑥 𝑣𝑣 𝑑𝑑 𝑏𝑏 � � ∆𝑡𝑡

= [ ( )]

𝑖𝑖 𝑖𝑖+1 𝑖𝑖 𝑇𝑇 � 𝑣𝑣 𝑡𝑡 − 𝑡𝑡

= 1 × 1000 𝑁𝑁 𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝛿𝛿 𝐸𝐸 � − � 𝑅𝑅𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑑𝑑𝑠𝑠𝑠𝑠𝑑𝑑

= 𝛿𝛿𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝛿𝛿𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 𝛿𝛿𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 − 𝛿𝛿𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

Figure 1 Map of the sampling sites along the Liesbeek River, Western Cape, South Africa (1: Mowbray site, 2: UCT Upper Campus site, 3: Skeleton Gorge site). The Cape Peninsula is shown, with the Liesbeek River inset.

http://dx.doi.org/10.4314/wsa.v44i4.16 Available on website http://www.wrc.org.za ISSN 1816-7950 (Online) = Water SA Vol. 44 No. 4 October 2018 Published under a Creative Commons Attribution Licence 675 As we did not measure ∆x/∆t across the entire breadth of period, using regressions of those residual values against time, the canal, notably near the edges where flow may be slower to ascertain whether the strength of coupling between stream- than midstream, our estimate of streamflow represents and rainwater isotope values changed during the course of the an upper limit of potential streamflow and most likely storm. We also constructed a local meteoric water line (LMWL) overestimates true streamflow. for Cape Town using previously published data (Harris et al., Total storm-flow T( , m3) was approximated as the sum of 2010) as the linear regression= × of rainwater × δ 18O against δ2H for streamflow over the =duration × of ×the storm, assuming constant Cape Town, across 4 years, from 2013 to 2016. flow between measurements, from the beginning of the storm We calculated the proportion of storm-flow∆𝑥𝑥 that was ∆𝑥𝑥 𝑣𝑣 𝑑𝑑 𝑏𝑏 � � until the flow returned𝑣𝑣 𝑑𝑑to base𝑏𝑏 levels� after� the storm rainfall-derived using a mass-balance model,∆𝑡𝑡 following the ∆𝑡𝑡 approach of Midgley and Scott (1994) and Midgley et al. (2001). We assumed only two inputs into the stream over the course = [ ( )] = [ ( )] (2) of the storm: baseflow and rainfall. We used the isotope values 𝑖𝑖 𝑖𝑖+1 𝑖𝑖 of the stream before𝑇𝑇 the� storm𝑣𝑣𝑖𝑖 as𝑡𝑡 our𝑖𝑖+1 estimate− 𝑡𝑡𝑖𝑖 of baseflow 𝑇𝑇 � 𝑣𝑣 𝑡𝑡 3 −-1 𝑡𝑡 where vi is the streamflow (m ·hr ) at time i and ti+1 – ti (h) (δbaseflow), the amount-weighted isotope values of the storm’s is the interval between time i and the following measurement cumulative rainfall (δrain) and the amount-weighted isotope at time i+1. values of the total river storm-flow δ( ) to calculate p, the = 1 × 1000 = stormflow1 × 1000 proportion of storm-flow that was derived from rainfall (Eq. 𝑁𝑁 𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑁𝑁 𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Stable isotope𝛿𝛿 𝐸𝐸 analyses − 4). This was done for each isotopic tracer separately, and then � 𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑑𝑑𝑠𝑠𝑠𝑠𝑑𝑑 � 𝛿𝛿 𝐸𝐸 � − � 𝑅𝑅 averaged, with propagated𝑅𝑅𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠 uncertainties.𝑑𝑑𝑠𝑠𝑠𝑠𝑑𝑑 Isotopic composition (δ 2H and δ 18O) of water samples was analysed by wavelength-scanned== × × cavity ring-down spectroscopy (WS-CRDS, Gupta et al., 2009) using a L2120-i = (4) 𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ∆𝑥𝑥 𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (Picarro Inc., 480 Oakmean𝑣𝑣 𝛿𝛿𝑑𝑑 𝑏𝑏Parkway,�− �𝛿𝛿 Sunnyvale, California, 𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 ∆𝑡𝑡 𝛿𝛿 − 𝛿𝛿 94085, USA; www.picarro.com𝛿𝛿𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 )− in𝛿𝛿 the𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Department of Biological 𝑠𝑠𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 RESULTS 𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Sciences, UCT. Liquid= water[ (samples were)] filtered through a 𝛿𝛿 − 𝛿𝛿 20 μm filter into 2mL sample vials and analysed within 24 h of 𝑖𝑖 𝑖𝑖+1 𝑖𝑖 A total of 53 mm of rainfall fell during the 52 h of the study collection. Isotope𝑇𝑇 ratios� are𝑣𝑣 expressed𝑡𝑡 − 𝑡𝑡 in ‰ as: period at the Mowbray study site (Fig. 2A). The flow rate of the Liesbeek River increased 100-fold during the storm, from 61 = 1 × 1000 (3) m3·h-1 (0.017 m3·s-1) before the storm, to a peak of 6 661 m3·h-1 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑁𝑁 𝑅𝑅 (1.850 m3·s-1) during the storm, before dropping back to 68 𝛿𝛿 𝐸𝐸 � − � where N is the atomic𝑅𝑅 mass𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑑𝑑 𝑠𝑠𝑠𝑠of𝑑𝑑 the heavy isotope of element m3·h-1 (0.019 m3·s-1) approximately 10 h after the last rain fell. 3 E and Rsample/Rstandard is the ratio of the heavy to light isotope Approximately 41 375.8 m flowed down the river during the 2 18 16 ( H/H or O/ O). Samples= were corrected to Vienna Standard storm event (total storm-flow, T). There was a strong correlation Mean Ocean Water (VSMOW)𝛿𝛿𝑠𝑠𝑡𝑡𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 using− 𝛿𝛿𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 a linear correction between streamflow and rainfall rates P( < 0.001, Pearson’s r = 𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 function based𝑠𝑠 on two laboratory𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠 𝑏𝑏 standards𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 that spanned 0.86, Figs 2B, 2C, 3A). the range of values in our𝛿𝛿 samples.− 𝛿𝛿 Accuracy was determined There was also a strong correlation between the isotopic using a quality control standard that was not used in standard composition of the rainwater and stormwater (Pearson’s r = corrections. Long-term precision and accuracy of this 0.741 and 0.775 for δ18O and δ2H, respectively, P < 0.001 in instrument were 0.2‰ and 1.5‰ for δ2H and 0.07‰ and both cases) (Figs 2D, 2E). The δ18O and δ2H values of rainwater 0.13‰ for δ18O, respectively. We propagated these uncertainties were more variable than those of the stream-water (Fig. 2D, appropriately (Genereux, 1998), conservatively treating total 2E). δ18O ranged from −2.02‰ to −6.06‰ in stream-water analytical uncertainty for isotope values as the combination and −2.95‰ to −7.75‰ in rainwater and δ2H ranged from of precision and accuracy. Due to the potential for organic −5.34‰ to −28.37‰ in stream-water and −8.60‰ to −40.06‰ contaminants to interfere with WS-CRDS measurements (West in rainwater. We found no significant relationship between et al., 2010; Brand et al., 2009), all analyses were run through stream- versus rainwater residuals (Fig. 3) and time, suggesting post-processing software (ChemCorrect version 1.2) to detect that the strength of the relationship between stream- and for possible organic contamination (West et al., 2011). No rainwater isotope values (Fig. 3B, 3C) (and indeed streamflow indications of contamination were seen for any of our river and and rainfall amounts (Fig. 3A)) was relatively uniform during rain samples. the course of the storm event. The isotopic composition of the rain and stream measured Statistical analyses during this storm was indistinguishable from the LMWL for the Cape Town area (derived from long-term data from Harris et al. All analyses were carried out in R (R Core Team, 2017) using (2010)), suggesting that rainfall is the principal source of water to the ‘tidyverse’ suite of packages (Wickham, 2017) for data streamflow within the section of river studied (Fig. 4). Notably, exploration and visualisation. Simple linear regressions were the amount-weighted isotope values for the Mowbray and UCT performed for: streamflow rate against rainfall rate, δ18O rainfall (δ18O = −4.79‰, δ2H = −20.09‰ and δ18O = −5.08‰, and δ2H of stream-water against those of rainwater and δ18O δ2H = −20.50‰, respectively, Fig. 4) were similar, and both against δ2H for stream-water and rainwater. Additionally, markedly isotopically heavier in both δ2H and δ18O than that we assessed whether the residuals in these linear models from Skeleton Gorge (δ18O = −5.99‰, δ2H = −22.48‰, Fig. 4). depended on a measurement’s timing within the sampling

http://dx.doi.org/10.4314/wsa.v44i4.16 Available on website http://www.wrc.org.za ISSN 1816-7950 (Online) = Water SA Vol. 44 No. 4 October 2018 Published under a Creative Commons Attribution Licence 676 Figure 2 Timeline of isotopic and hydrological values throughout the winter storm event during which sampling was carried out (ca. 54 h range of sampling): A) cumulative and B) each measurement’s rainfall rate (m3·h-1), C) streamflow rate (m3·h-1) of the Liesbeek River, and D) δ18O (‰) and E) δ2H (‰) values for both the rain and stream-water.

Figure 3 Scatter-plots of rain- versus stream-water A) amounts (m3·h-1), B) δ18O (‰) and C) δ2H (‰) values, where there were rainfall measurements contemporaneous with stream-water measurements (i.e. excluding those stream-water measurements when there was no rainfall for isotope analyses). The trend-lines represent the simple linear regressions of stream-water values against rainwater values, with 95% confidence intervals in grey. The R2-values, slopes, and P-values of the slope-terms are inset for the linear regressions in each case. None of these models’ residuals were found to depend on when measurements were taken during sampling (all R2 < 0.15, all slopes ≈ 0, all P >> 0.05), following linear regressions of residual values against time.

Following the mass-balance model (Eq. 4), we calculated the contribution of rainfall to the storm-flow in the Liesbeek River. Using the amount-weighted values for streamflow, rainfall and storm-flow (Table 1), we calculated that 101.1% (± 10.7%) of storm-flow was derived from rainfall. This suggests that stream-water was almost entirely (> 90%) derived from recent rainwater.

DISCUSSION

Here we have presented the isotopic similarity of river and rainwater throughout the storm event, a strong correlation between streamflow and rainfall rates and a large proportion (> 90%) of rainwater making up the streamflow during the storm event. We therefore conclude that overland-flow is the predominant driver of the river’s flow rate during this rainfall event. The isotopic similarity between stream- and rainwater, and the degree to which these values track each other during the storm event, suggest that recent rainfall is indeed the majority contributor to storm-flow. This directly contrasts

http://dx.doi.org/10.4314/wsa.v44i4.16 Available on website http://www.wrc.org.za ISSN 1816-7950 (Online) = Water SA Vol. 44 No. 4 October 2018 Published under a Creative Commons Attribution Licence 677 into rivers and out to sea, without recharging soil reservoirs or aquifers, thereby increasing an area’s susceptibility to water shortages (Bruijnzeel, 2004). A key implication of our study is that a potentially valuable source of water for the over-extended City of Cape Town’s water supply is being lost directly to the sea, without influencing aquifer recharge. Considering the dire water crisis that the City currently finds itself in, any additional water sources that could augment its supply merit investigation. When performed correctly, stormwater harvesting has been identified as beneficial for ecological, environmental and social reasons (Fletcher et al., 2007; Mitchell et al., 2007; Inamdar et al., 2013). While the City of Cape Town has already begun emergency development of aquifer extraction for municipal water use (Evans, 2018), recharge of these aquifers must be considered. Our study supports previous work suggesting that Figure 4 artificial recharge, as well as domestic water use, could include 2 18 Scatter plot of δ H (‰) versus δ O (‰) for storm rainwater (blue circles), stormwater harvested from the Liesbeek River (Inamdar et al., and other points of interest. The sample LMWL (grey dashes) (Harris et al., 2013; Fisher-Jeffers et al., 2017). 2010) has the form δ2H = 5.015 × δ18O + 5.777. The black point represents baseflow (i.e. the amount-weighted mean isotope values for the three pre-storm stream-water samples). The grey square (MAP) represents the ACKNOWLEDGEMENTS amount-weighted mean annual rainfall isotope value (Harris et al., 2010). The dark blue points represent the amount-weighted mean isotope We acknowledge the contributions of R de Wet, K Atkins, V values for cumulative storm rainwater from near the urban stream in Coetzee, M Cruickshank, D Hegarty and L Van Der Pas during the suburb of Mowbray, from the slightly elevated Upper Campus of data collection and processing. Prof JJ Midgley contributed to the University of Cape Town (UCT), and from the mountainous Skeleton the experimental design. Gorge. The solid blue trend-line represents the simple linear regression of δ2H against δ18O with 95% confidence intervals in grey. The R2-values, slopes, and P-values of the slope-terms are inset. SUPPORTING INFORMATION Data-sets and analyses in the form of R-scripts are available online at https://github.com/rvanmazijk/ Table 1 Input data and results from the mass-balance model (Eq. 4). Liesbeek-River-isotopics. Means presented here are amount-weighted. Proportions are unitless. ORCID

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